{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/Taehee-K/Brain-Tumor-Classification/blob/main/code/AlexNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mbF-t5mNPJP6"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "path = os.path.dirname(os.path.abspath(__file__))\n",
    "os.chdir(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0mlQ8IxbPV6x"
   },
   "source": [
    "# Split Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nfurc7XoQROn"
   },
   "source": [
    "* Train, Validation, Test 划分数据文件夹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "vdgUY4_vPVS_"
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    " \n",
    "original_dataset_dir = '../BrainTumorData'   \n",
    "classes_list = os.listdir(original_dataset_dir) \n",
    " \n",
    "base_dir = '../splitted'                           # train-validation 划分并存储数据\n",
    "os.mkdir(base_dir)\n",
    " \n",
    "train_dir = os.path.join(base_dir, 'train')       # train data\n",
    "os.mkdir(train_dir)\n",
    "test_dir = os.path.join(base_dir, 'val')          # test data\n",
    "os.mkdir(test_dir)\n",
    "\n",
    "for cls in classes_list:     \n",
    "    os.mkdir(os.path.join(train_dir, cls))\n",
    "    os.mkdir(os.path.join(test_dir, cls))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ncEjsRz_QVoR"
   },
   "source": [
    "* 将数据拆分为 Train:Test 8:2  \n",
    "* 检查每个类的数据数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8aPlJ4qIQKyS",
    "outputId": "2088d200-e874-4730-99c6-ac87cec7ea7b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size( Brain Tumor ):  2010\n",
      "Test size( Brain Tumor ):  503\n",
      "Train size( Healthy ):  1669\n",
      "Test size( Healthy ):  418\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    " \n",
    "for cls in classes_list:\n",
    "    path = os.path.join(original_dataset_dir, cls)\n",
    "    fnames = os.listdir(path)\n",
    " \n",
    "    train_size = math.floor(len(fnames) * 0.8)\n",
    "    test_size = math.floor(len(fnames) * 0.2)\n",
    "    \n",
    "    train_fnames = fnames[:train_size]\n",
    "    print(\"Train size(\",cls,\"): \", len(train_fnames))\n",
    "    for fname in train_fnames:\n",
    "        src = os.path.join(path, fname)\n",
    "        dst = os.path.join(os.path.join(train_dir, cls), fname)\n",
    "        shutil.copyfile(src, dst)\n",
    "        \n",
    "    test_fnames = fnames[train_size:]\n",
    "    print(\"Test size(\",cls,\"): \", len(test_fnames))\n",
    "    for fname in test_fnames:\n",
    "        src = os.path.join(path, fname)\n",
    "        dst = os.path.join(os.path.join(test_dir, cls), fname)\n",
    "        shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gY3Ec2iGQoQh"
   },
   "source": [
    "# Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "4xWouD7KQnuo"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load Data\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.datasets import ImageFolder \n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# Pytorch --> MLP, CNN\n",
    "import torch\n",
    "import torch.nn as nn \n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "21eJSOYPQw5f",
    "outputId": "d0a01b6c-2e35-4910-9fd7-21939b7dc446"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "  DEVICE = torch.device('cuda')\n",
    "else:\n",
    "  DEVICE = torch.device('cpu')\n",
    "print(DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "zIDQtchdQxGe"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "EPOCH = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QkMRfTeJQ2j-",
    "outputId": "667504be-dad4-4fcf-d89a-f7ae95e1e619"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Brain Tumor', 'Healthy']\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "n_classes = len(classes_list)\n",
    "print(classes_list)           # 要分类的类别\n",
    "print(n_classes)      # 分类簇数量：2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QAqMDgOiUvSK"
   },
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "hwA82Zz6RKq3"
   },
   "outputs": [],
   "source": [
    "transform_base = transforms.Compose([transforms.Resize((227,227)),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     transforms.Grayscale(num_output_channels=1)]) \n",
    "train_dataset = ImageFolder(root='../splitted/train', transform=transform_base)\n",
    "test_dataset = ImageFolder(root='../splitted/val', transform=transform_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JvRneP0vRNsL",
    "outputId": "57371c20-872e-482e-c730-7a4ec40892de"
   },
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset,batch_size=BATCH_SIZE, shuffle=True, num_workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lo4OE5Wdb3xI"
   },
   "source": [
    "* 检查数据数量和格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sqysFnG7bxZs",
    "outputId": "25f9ae04-76d7-45e8-cff0-1e2107820721"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: torch.Size([64, 1, 227, 227]) type: torch.FloatTensor\n",
      "y_train: torch.Size([64]) type: torch.LongTensor\n"
     ]
    }
   ],
   "source": [
    "for (X_train, y_train) in train_loader:\n",
    "    print('X_train:', X_train.size(),'type:', X_train.type())\n",
    "    print('y_train:', y_train.size(),'type:', y_train.type())\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hb9ryNtsTf8W"
   },
   "source": [
    "* 查看数据照片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "id": "3PYcSlVGTdRx",
    "outputId": "791694cf-646f-4c6e-aa1c-7de7788067f4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pltsize = 2\n",
    "plt.figure(figsize=(10 * pltsize, pltsize))\n",
    "for i in range(10):\n",
    "    plt.subplot(1, 10, i + 1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(X_train[i, :, :, :].numpy().reshape(227, 227), cmap = \"gray_r\")\n",
    "    plt.title('Class: ' + str(y_train[i].item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qWITu3WVRI6n"
   },
   "source": [
    "# AlexNet "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H4I1L6cOVLGH"
   },
   "source": [
    "* AlexNet Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O0IhBUW3R-vu"
   },
   "source": [
    "**ImageNet Classification with Deep Convolutional\n",
    "Neural Networks AlexNet** \n",
    "\n",
    "总共由5个卷积层和3个全连接层组成\n",
    "\n",
    "Input layer -> Conv1 -> MaxPool1 -> Norm1 -> Conv2 -> MaxPool2 -> Norm2 -> Conv3 -> Conv4 -> Conv5 -> MaxPool3 -> Dropout1 -> FC1 ->  Dropout2 -> FC2 -> Output layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RBSODlK0SEiG"
   },
   "source": [
    "![image.png]()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M9yo9J_ORsdu",
    "outputId": "a38dd804-9658-4ec2-a8a1-e65639f5c123"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 96, 55, 55]          11,712\n",
      "              ReLU-2           [-1, 96, 55, 55]               0\n",
      "         MaxPool2d-3           [-1, 96, 27, 27]               0\n",
      "       BatchNorm2d-4           [-1, 96, 27, 27]             192\n",
      "            Conv2d-5          [-1, 256, 27, 27]         614,656\n",
      "              ReLU-6          [-1, 256, 27, 27]               0\n",
      "         MaxPool2d-7          [-1, 256, 13, 13]               0\n",
      "       BatchNorm2d-8          [-1, 256, 13, 13]             512\n",
      "            Conv2d-9          [-1, 384, 13, 13]         885,120\n",
      "             ReLU-10          [-1, 384, 13, 13]               0\n",
      "           Conv2d-11          [-1, 384, 13, 13]       1,327,488\n",
      "             ReLU-12          [-1, 384, 13, 13]               0\n",
      "           Conv2d-13          [-1, 256, 13, 13]         884,992\n",
      "             ReLU-14          [-1, 256, 13, 13]               0\n",
      "        MaxPool2d-15            [-1, 256, 6, 6]               0\n",
      "          Flatten-16                 [-1, 9216]               0\n",
      "          Dropout-17                 [-1, 9216]               0\n",
      "           Linear-18                 [-1, 4096]      37,752,832\n",
      "             ReLU-19                 [-1, 4096]               0\n",
      "          Dropout-20                 [-1, 4096]               0\n",
      "           Linear-21                 [-1, 4096]      16,781,312\n",
      "             ReLU-22                 [-1, 4096]               0\n",
      "           Linear-23                    [-1, 2]           8,194\n",
      "================================================================\n",
      "Total params: 58,267,010\n",
      "Trainable params: 58,267,010\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.20\n",
      "Forward/backward pass size (MB): 12.01\n",
      "Params size (MB): 222.27\n",
      "Estimated Total Size (MB): 234.48\n",
      "----------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:63: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n"
     ]
    }
   ],
   "source": [
    "class AlexNet(nn.Module): # AlexNet\n",
    "    def __init__(self, n_classes = 2):   \n",
    "        super(AlexNet, self).__init__()\n",
    "\n",
    "        # 1st conv layer\n",
    "        self.Conv_1 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 1, out_channels = 96, kernel_size = 11, stride = 4, padding = 0),\n",
    "          nn.ReLU(),\n",
    "          nn.MaxPool2d(kernel_size = 3, stride = 2),\n",
    "          nn.BatchNorm2d(96))\n",
    "        \n",
    "        # 2nd conv layer\n",
    "        self.Conv_2 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 96, out_channels = 256, kernel_size = 5, stride = 1, padding = 2),\n",
    "          nn.ReLU(),\n",
    "          nn.MaxPool2d(kernel_size = 3, stride = 2),\n",
    "          nn.BatchNorm2d(256))\n",
    "        \n",
    "        # 3rd conv layer\n",
    "        self.Conv_3 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 256, out_channels = 384, kernel_size = 3, stride = 1, padding = 1),\n",
    "          nn.ReLU())\n",
    "        \n",
    "        # 4th conv layer\n",
    "        self.Conv_4 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 384, out_channels = 384, kernel_size = 3, stride = 1, padding = 1),\n",
    "          nn.ReLU())\n",
    "        \n",
    "        # 5th conv layer\n",
    "        self.Conv_5 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 384, out_channels = 256, kernel_size = 3, stride = 1, padding = 1),\n",
    "          nn.ReLU(),\n",
    "          nn.MaxPool2d(kernel_size = 3, stride = 2))\n",
    "        \n",
    "        # 1st fully connected layer\n",
    "        self.FC1 = nn.Sequential(\n",
    "          nn.Flatten(),\n",
    "          nn.Dropout(0.5),\n",
    "          nn.Linear(9216, 4096),\n",
    "          nn.ReLU())\n",
    "\n",
    "        # 2nd fully connected layer\n",
    "        self.FC2 = nn.Sequential(\n",
    "          nn.Dropout(0.5),\n",
    "          nn.Linear(4096, 4096),\n",
    "          nn.ReLU())\n",
    "\n",
    "        # 3rd fully connected layer --> output layer\n",
    "        self.FC3 = nn.Sequential(\n",
    "          nn.Linear(4096, n_classes))\n",
    "    \n",
    "    def forward(self, x):   # AlexNet forward propagation\n",
    "\n",
    "        out = self.Conv_1(x)    \n",
    "        out = self.Conv_2(out)\n",
    "        out = self.Conv_3(out)\n",
    "        out = self.Conv_4(out)\n",
    "        out = self.Conv_5(out)\n",
    "        out = self.FC1(out)\n",
    "        out = self.FC2(out)\n",
    "        out = self.FC3(out)\n",
    "\n",
    "        return F.log_softmax(out) # softmax 통해 최종 output 계산\n",
    "\n",
    "# print model summary\n",
    "alexnet = AlexNet().to(DEVICE)\n",
    "summary(alexnet, (1, 227, 227)) # summary code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-Cv7O0o0Rsw1"
   },
   "outputs": [],
   "source": [
    "optimizer = optim.Adam(alexnet.parameters(), lr=0.001)\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rooJ91V3U86Y"
   },
   "source": [
    "* Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NBMOFujjU9iL"
   },
   "outputs": [],
   "source": [
    "def train(model, train_loader, optimizer):\n",
    "    model.train()                         # AlexNet 模型训练状态\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(DEVICE), target.to(DEVICE)  # data, 分配给DEVICE的目标值\n",
    "        optimizer.zero_grad()                              # 初始化优化器梯度值\n",
    "        output = model(data)                               # 使用分配的数据计算输出\n",
    "        loss =  criterion(output, target)                  # 使用交叉熵损失计算损失\n",
    "        loss.backward()                                    # 计算损失反向传播\n",
    "        optimizer.step()                                   # 参数更新"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9RxzeapWVC8-"
   },
   "source": [
    "* Evaluate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QzZpfMSrVDpF"
   },
   "outputs": [],
   "source": [
    "def evaluate(model, test_loader):\n",
    "    model.eval()      \n",
    "    test_loss = 0     \n",
    "    correct = 0       \n",
    "    \n",
    "    with torch.no_grad(): \n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(DEVICE), target.to(DEVICE)     # data, 分配给DEVICE的目标值\n",
    "            output = model(data)                                  # 输出计算\n",
    "            test_loss += criterion(output, target).item()         # 计算损失（添加到总损失）\n",
    "            pred = output.max(1, keepdim=True)[1]                 # 预测计算出的向量值中具有最大值的类\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item() # 计算正确预测值\n",
    "   \n",
    "    test_loss /= len(test_loader.dataset)                         # 平均损失\n",
    "    test_accuracy = 100. * correct / len(test_loader.dataset)     # 测试（验证）数据准确性\n",
    "    return test_loss, test_accuracy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LfbgiyabVnIW"
   },
   "source": [
    "## Train AlexNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rmk9kEN2VpbV",
    "outputId": "8f31424e-d502-4296-d288-af805a5a041b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  cpuset_checked))\n",
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:63: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------- epoch 1 ----------------\n",
      "train Loss: 0.0076, Accuracy: 77.85%\n",
      "val Loss: 0.0093, Accuracy: 74.81%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 2 ----------------\n",
      "train Loss: 0.0080, Accuracy: 73.80%\n",
      "val Loss: 0.0086, Accuracy: 74.38%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 3 ----------------\n",
      "train Loss: 0.0063, Accuracy: 82.09%\n",
      "val Loss: 0.0094, Accuracy: 67.97%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 4 ----------------\n",
      "train Loss: 0.0064, Accuracy: 81.27%\n",
      "val Loss: 0.0156, Accuracy: 76.22%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 5 ----------------\n",
      "train Loss: 0.0132, Accuracy: 68.66%\n",
      "val Loss: 0.0201, Accuracy: 71.12%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 6 ----------------\n",
      "train Loss: 0.0056, Accuracy: 85.65%\n",
      "val Loss: 0.0185, Accuracy: 72.96%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 7 ----------------\n",
      "train Loss: 0.0042, Accuracy: 88.20%\n",
      "val Loss: 0.0129, Accuracy: 66.45%\n",
      "Completed in 0m 35s\n",
      "-------------- epoch 8 ----------------\n",
      "train Loss: 0.0024, Accuracy: 94.29%\n",
      "val Loss: 0.0133, Accuracy: 81.87%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 9 ----------------\n",
      "train Loss: 0.0043, Accuracy: 88.28%\n",
      "val Loss: 0.0168, Accuracy: 72.20%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 10 ----------------\n",
      "train Loss: 0.0016, Accuracy: 96.68%\n",
      "val Loss: 0.0111, Accuracy: 81.22%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 11 ----------------\n",
      "train Loss: 0.0013, Accuracy: 96.90%\n",
      "val Loss: 0.0121, Accuracy: 83.28%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 12 ----------------\n",
      "train Loss: 0.0025, Accuracy: 95.22%\n",
      "val Loss: 0.0213, Accuracy: 79.15%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 13 ----------------\n",
      "train Loss: 0.0016, Accuracy: 95.49%\n",
      "val Loss: 0.0227, Accuracy: 77.85%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 14 ----------------\n",
      "train Loss: 0.0009, Accuracy: 98.02%\n",
      "val Loss: 0.0083, Accuracy: 86.86%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 15 ----------------\n",
      "train Loss: 0.0011, Accuracy: 97.25%\n",
      "val Loss: 0.0204, Accuracy: 86.21%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 16 ----------------\n",
      "train Loss: 0.0010, Accuracy: 97.96%\n",
      "val Loss: 0.0099, Accuracy: 86.43%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 17 ----------------\n",
      "train Loss: 0.0004, Accuracy: 99.29%\n",
      "val Loss: 0.0103, Accuracy: 87.84%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 18 ----------------\n",
      "train Loss: 0.0007, Accuracy: 98.37%\n",
      "val Loss: 0.0100, Accuracy: 86.75%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 19 ----------------\n",
      "train Loss: 0.0005, Accuracy: 98.99%\n",
      "val Loss: 0.0089, Accuracy: 88.27%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 20 ----------------\n",
      "train Loss: 0.0011, Accuracy: 96.93%\n",
      "val Loss: 0.0230, Accuracy: 82.52%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 21 ----------------\n",
      "train Loss: 0.0014, Accuracy: 95.71%\n",
      "val Loss: 0.0130, Accuracy: 78.83%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 22 ----------------\n",
      "train Loss: 0.0002, Accuracy: 99.51%\n",
      "val Loss: 0.0244, Accuracy: 86.54%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 23 ----------------\n",
      "train Loss: 0.0008, Accuracy: 98.18%\n",
      "val Loss: 0.0290, Accuracy: 83.17%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 24 ----------------\n",
      "train Loss: 0.0009, Accuracy: 98.15%\n",
      "val Loss: 0.0231, Accuracy: 82.08%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 25 ----------------\n",
      "train Loss: 0.0008, Accuracy: 97.88%\n",
      "val Loss: 0.0182, Accuracy: 86.97%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 26 ----------------\n",
      "train Loss: 0.0007, Accuracy: 98.31%\n",
      "val Loss: 0.0091, Accuracy: 89.03%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 27 ----------------\n",
      "train Loss: 0.0013, Accuracy: 97.36%\n",
      "val Loss: 0.0025, Accuracy: 94.03%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 28 ----------------\n",
      "train Loss: 0.0004, Accuracy: 99.35%\n",
      "val Loss: 0.0137, Accuracy: 86.43%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 29 ----------------\n",
      "train Loss: 0.0025, Accuracy: 95.68%\n",
      "val Loss: 0.0182, Accuracy: 83.82%\n",
      "Completed in 0m 36s\n",
      "-------------- epoch 30 ----------------\n",
      "train Loss: 0.0002, Accuracy: 99.62%\n",
      "val Loss: 0.0224, Accuracy: 84.47%\n",
      "Completed in 0m 36s\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import copy\n",
    " \n",
    "def train_model(model ,train_loader, val_loader, optimizer, num_epochs = 30):\n",
    "    best_acc = 0.0 \n",
    "    best_model_wts = copy.deepcopy(model.state_dict()) \n",
    " \n",
    "    for epoch in range(1, num_epochs + 1):\n",
    "        since = time.time()                                     # 学习时间计算\n",
    "        train(model, train_loader, optimizer)                   # 利用训练数据进行学习\n",
    "        train_loss, train_acc = evaluate(model, train_loader)   # train_loss, train_acc 计算\n",
    "        val_loss, val_acc = evaluate(model, val_loader)         # valid_loss, valid_acc 计算\n",
    "        \n",
    "        if val_acc>best_acc:  # update best accuracy\n",
    "            best_acc = val_acc\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())\n",
    "        \n",
    "        time_elapsed = time.time() - since # 学习时间输出\n",
    "        print('-------------- epoch {} ----------------'.format(epoch))\n",
    "        print('train Loss: {:.4f}, Accuracy: {:.2f}%'.format(train_loss, train_acc))   \n",
    "        print('val Loss: {:.4f}, Accuracy: {:.2f}%'.format(val_loss, val_acc))\n",
    "        print('Completed in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) \n",
    "\n",
    "    model.load_state_dict(best_model_wts)  \n",
    "    return model\n",
    "\n",
    "model = train_model(alexnet ,train_loader, test_loader, optimizer)  \t# 训练模型\n",
    "torch.save(model,'AlexNet.pt') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wDIf1rOTVtqw"
   },
   "source": [
    "## Test AlexNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Vck5U8sCcPgR",
    "outputId": "f766d109-0599-4bef-8b37-60da18847510"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  cpuset_checked))\n",
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:63: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 94.0282\n"
     ]
    }
   ],
   "source": [
    "model=torch.load('AlexNet.pt')\n",
    "model.eval()\n",
    "loss, acc = evaluate(model, test_loader)\n",
    "\n",
    "print('Test Accuracy: {:.4f}'.format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ox29vVfeW4gv"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "def prediction(model, data_loader):\n",
    "    model.eval()\n",
    "    predlist=torch.zeros(0,dtype=torch.long, device='cpu')\n",
    "    lbllist=torch.zeros(0,dtype=torch.long, device='cpu')\n",
    "    \n",
    "    with torch.no_grad():\n",
    "      for i, (data, label) in enumerate(data_loader):\n",
    "        data = data.to(DEVICE)        # 将数据分配给DEVICE\n",
    "        label = label.to(DEVICE)      # 分配标签值DEVICE\n",
    "        outputs = model(data)         # 预测\n",
    "        _, preds = torch.max(outputs, 1)  # 预测概率最高的类别\n",
    "\n",
    "        # 追加批次单位预测\n",
    "        predlist=torch.cat([predlist,preds.view(-1).cpu()])\n",
    "        lbllist=torch.cat([lbllist,label.view(-1).cpu()])\n",
    "        \n",
    "    # Classification Report\n",
    "    print(classification_report(lbllist.numpy(), predlist.numpy())) # 按类的 accuracy, recall, f1-score \n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6Qzh5k18iL0E",
    "outputId": "7910565f-332e-4136-e86d-08b014d0dde6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  cpuset_checked))\n",
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:63: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.93      0.94       503\n",
      "           1       0.92      0.95      0.94       418\n",
      "\n",
      "    accuracy                           0.94       921\n",
      "   macro avg       0.94      0.94      0.94       921\n",
      "weighted avg       0.94      0.94      0.94       921\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prediction(model, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "X4VhHCl6k6IQ"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyN0+1Dm9w3Kuhe+IaivnAUd",
   "include_colab_link": true,
   "name": "AlexNet.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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